Bibliographic record
Abstract
In the 10 provinces and 2 territories of Canada in 2000, but not in 1990, the total number of types of gambling activities was positively associated with rates of robbery (p<.05). Controls for other social variables did not eliminate these associations. With so many correlations in the present study the likelihood of a Type I error was quite large. Alpha was adjusted to control that likelihood. Statistical analysis now required even stronger evidence before concluding that there were significant relationships between crime and gambling variables or among gambling variables. In the 10 provinces of Canada in 1999/2000, the total numbers of electronic gambling machines for each province was associated with rates of theft over $5000 (p<.01). In 1990 there were positive associations found for burglary with off-track betting and race/sportsbooks; motor vehicle theft with off-track betting, and race/sportsbooks; rate of theft with casinos; quarter horse racing with thoroughbred racing. In 2000 there were positive associations for robbery with casinos and slot machines; casinos with slot machines; scratch tickets with raffles, break-open tickets, sports tickets, and charitable bingo; raffles with break-open tickets, sports tickets, and charitable bingo; break-open tickets with sports tickets; charitable bingo with break-open tickets and sports tickets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".